# Copyright 2024 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#

"""Transformers for Google GenAI SDK."""

import base64
from collections.abc import Iterable, Mapping
from enum import Enum, EnumMeta
import inspect
import io
import logging
import re
import sys
import time
import typing
from typing import Any, GenericAlias, Optional, Union

if typing.TYPE_CHECKING:
  import PIL.Image

import pydantic

from . import _api_client
from . import types

logger = logging.getLogger('google_genai._transformers')

if sys.version_info >= (3, 10):
  VersionedUnionType = typing.types.UnionType
  _UNION_TYPES = (typing.Union, typing.types.UnionType)
else:
  VersionedUnionType = typing._UnionGenericAlias
  _UNION_TYPES = (typing.Union,)


def _resource_name(
    client: _api_client.ApiClient,
    resource_name: str,
    *,
    collection_identifier: str,
    collection_hierarchy_depth: int = 2,
):
  # pylint: disable=line-too-long
  """Prepends resource name with project, location, collection_identifier if needed.

  The collection_identifier will only be prepended if it's not present
  and the prepending won't violate the collection hierarchy depth.
  When the prepending condition doesn't meet, returns the input
  resource_name.

  Args:
    client: The API client.
    resource_name: The user input resource name to be completed.
    collection_identifier: The collection identifier to be prepended. See
      collection identifiers in https://google.aip.dev/122.
    collection_hierarchy_depth: The collection hierarchy depth. Only set this
      field when the resource has nested collections. For example,
      `users/vhugo1802/events/birthday-dinner-226`, the collection_identifier is
      `users` and collection_hierarchy_depth is 4. See nested collections in
      https://google.aip.dev/122.

  Example:

    resource_name = 'cachedContents/123'
    client.vertexai = True
    client.project = 'bar'
    client.location = 'us-west1'
    _resource_name(client, 'cachedContents/123',
      collection_identifier='cachedContents')
    returns: 'projects/bar/locations/us-west1/cachedContents/123'

  Example:

    resource_name = 'projects/foo/locations/us-central1/cachedContents/123'
    # resource_name = 'locations/us-central1/cachedContents/123'
    client.vertexai = True
    client.project = 'bar'
    client.location = 'us-west1'
    _resource_name(client, resource_name,
      collection_identifier='cachedContents')
    returns: 'projects/foo/locations/us-central1/cachedContents/123'

  Example:

    resource_name = '123'
    # resource_name = 'cachedContents/123'
    client.vertexai = False
    _resource_name(client, resource_name,
      collection_identifier='cachedContents')
    returns 'cachedContents/123'

  Example:
    resource_name = 'some/wrong/cachedContents/resource/name/123'
    resource_prefix = 'cachedContents'
    client.vertexai = False
    # client.vertexai = True
    _resource_name(client, resource_name,
      collection_identifier='cachedContents')
    returns: 'some/wrong/cachedContents/resource/name/123'

  Returns:
    The completed resource name.
  """
  should_prepend_collection_identifier = (
      not resource_name.startswith(f'{collection_identifier}/')
      # Check if prepending the collection identifier won't violate the
      # collection hierarchy depth.
      and f'{collection_identifier}/{resource_name}'.count('/') + 1
      == collection_hierarchy_depth
  )
  if client.vertexai:
    if resource_name.startswith('projects/'):
      return resource_name
    elif resource_name.startswith('locations/'):
      return f'projects/{client.project}/{resource_name}'
    elif resource_name.startswith(f'{collection_identifier}/'):
      return f'projects/{client.project}/locations/{client.location}/{resource_name}'
    elif should_prepend_collection_identifier:
      return f'projects/{client.project}/locations/{client.location}/{collection_identifier}/{resource_name}'
    else:
      return resource_name
  else:
    if should_prepend_collection_identifier:
      return f'{collection_identifier}/{resource_name}'
    else:
      return resource_name


def t_model(client: _api_client.ApiClient, model: str):
  if not model:
    raise ValueError('model is required.')
  if client.vertexai:
    if (
        model.startswith('projects/')
        or model.startswith('models/')
        or model.startswith('publishers/')
    ):
      return model
    elif '/' in model:
      publisher, model_id = model.split('/', 1)
      return f'publishers/{publisher}/models/{model_id}'
    else:
      return f'publishers/google/models/{model}'
  else:
    if model.startswith('models/'):
      return model
    elif model.startswith('tunedModels/'):
      return model
    else:
      return f'models/{model}'


def t_models_url(api_client: _api_client.ApiClient, base_models: bool) -> str:
  if api_client.vertexai:
    if base_models:
      return 'publishers/google/models'
    else:
      return 'models'
  else:
    if base_models:
      return 'models'
    else:
      return 'tunedModels'


def t_extract_models(
    api_client: _api_client.ApiClient, response: dict
) -> list[types.Model]:
  if not response:
    return []
  elif response.get('models') is not None:
    return response.get('models')
  elif response.get('tunedModels') is not None:
    return response.get('tunedModels')
  elif response.get('publisherModels') is not None:
    return response.get('publisherModels')
  elif (
      response.get('httpHeaders') is not None
      and response.get('jsonPayload') is None
  ):
    return []
  else:
    logger.warning('Cannot determine the models type.')
    logger.debug('Cannot determine the models type for response: %s', response)
    return []


def t_caches_model(api_client: _api_client.ApiClient, model: str):
  model = t_model(api_client, model)
  if not model:
    return None
  if model.startswith('publishers/') and api_client.vertexai:
    # vertex caches only support model name start with projects.
    return (
        f'projects/{api_client.project}/locations/{api_client.location}/{model}'
    )
  elif model.startswith('models/') and api_client.vertexai:
    return f'projects/{api_client.project}/locations/{api_client.location}/publishers/google/{model}'
  else:
    return model


def pil_to_blob(img) -> types.Blob:
  try:
    import PIL.PngImagePlugin

    PngImagePlugin = PIL.PngImagePlugin
  except ImportError:
    PngImagePlugin = None

  bytesio = io.BytesIO()
  if (
      PngImagePlugin is not None
      and isinstance(img, PngImagePlugin.PngImageFile)
      or img.mode == 'RGBA'
  ):
    img.save(bytesio, format='PNG')
    mime_type = 'image/png'
  else:
    img.save(bytesio, format='JPEG')
    mime_type = 'image/jpeg'
  bytesio.seek(0)
  data = bytesio.read()
  return types.Blob(mime_type=mime_type, data=data)


PartType = Union[types.Part, types.PartDict, str, 'PIL.Image.Image']


def t_part(client: _api_client.ApiClient, part: PartType) -> types.Part:
  try:
    import PIL.Image

    PIL_Image = PIL.Image.Image
  except ImportError:
    PIL_Image = None

  if not part:
    raise ValueError('content part is required.')
  if isinstance(part, str):
    return types.Part(text=part)
  if PIL_Image is not None and isinstance(part, PIL_Image):
    return types.Part(inline_data=pil_to_blob(part))
  if isinstance(part, types.File):
    if not part.uri or not part.mime_type:
      raise ValueError('file uri and mime_type are required.')
    return types.Part.from_uri(file_uri=part.uri, mime_type=part.mime_type)
  else:
    return part


def t_parts(
    client: _api_client.ApiClient, parts: Union[list, PartType]
) -> list[types.Part]:
  if not parts:
    raise ValueError('content parts are required.')
  if isinstance(parts, list):
    return [t_part(client, part) for part in parts]
  else:
    return [t_part(client, parts)]


def t_image_predictions(
    client: _api_client.ApiClient,
    predictions: Optional[Iterable[Mapping[str, Any]]],
) -> list[types.GeneratedImage]:
  if not predictions:
    return None
  images = []
  for prediction in predictions:
    if prediction.get('image'):
      images.append(
          types.GeneratedImage(
              image=types.Image(
                  gcs_uri=prediction['image']['gcsUri'],
                  image_bytes=prediction['image']['imageBytes'],
              )
          )
      )
  return images


ContentType = Union[types.Content, types.ContentDict, PartType]


def t_content(
    client: _api_client.ApiClient,
    content: ContentType,
):
  if not content:
    raise ValueError('content is required.')
  if isinstance(content, types.Content):
    return content
  if isinstance(content, dict):
    return types.Content.model_validate(content)
  return types.Content(role='user', parts=t_parts(client, content))


def t_contents_for_embed(
    client: _api_client.ApiClient,
    contents: Union[list[types.Content], list[types.ContentDict], ContentType],
):
  if client.vertexai and isinstance(contents, list):
    # TODO: Assert that only text is supported.
    return [t_content(client, content).parts[0].text for content in contents]
  elif client.vertexai:
    return [t_content(client, contents).parts[0].text]
  elif isinstance(contents, list):
    return [t_content(client, content) for content in contents]
  else:
    return [t_content(client, contents)]


def t_contents(
    client: _api_client.ApiClient,
    contents: Union[list[types.Content], list[types.ContentDict], ContentType],
):
  if not contents:
    raise ValueError('contents are required.')
  if isinstance(contents, list):
    return [t_content(client, content) for content in contents]
  else:
    return [t_content(client, contents)]


def handle_null_fields(schema: dict[str, Any]):
  """Process null fields in the schema so it is compatible with OpenAPI.

  The OpenAPI spec does not support 'type: 'null' in the schema. This function
  handles this case by adding 'nullable: True' to the null field and removing
  the {'type': 'null'} entry.

  https://swagger.io/docs/specification/v3_0/data-models/data-types/#null

  Example of schema properties before and after handling null fields:
    Before:
      {
        "name": {
          "title": "Name",
          "type": "string"
        },
        "total_area_sq_mi": {
          "anyOf": [
            {
              "type": "integer"
            },
            {
              "type": "null"
            }
          ],
          "default": None,
          "title": "Total Area Sq Mi"
        }
      }

    After:
      {
        "name": {
          "title": "Name",
          "type": "string"
        },
        "total_area_sq_mi": {
          "type": "integer",
          "nullable": true,
          "default": None,
          "title": "Total Area Sq Mi"
        }
      }
  """
  if schema.get('type', None) == 'null':
    schema['nullable'] = True
    del schema['type']
  elif 'anyOf' in schema:
    for item in schema['anyOf']:
      if 'type' in item and item['type'] == 'null':
        schema['nullable'] = True
        schema['anyOf'].remove({'type': 'null'})
        if len(schema['anyOf']) == 1:
          # If there is only one type left after removing null, remove the anyOf field.
          for key,val in schema['anyOf'][0].items():
            schema[key] = val
          del schema['anyOf']


def process_schema(
    schema: dict[str, Any],
    client: Optional[_api_client.ApiClient] = None,
    defs: Optional[dict[str, Any]] = None,
    *,
    order_properties: bool = True,
):
  """Updates the schema and each sub-schema inplace to be API-compatible.

  - Removes the `title` field from the schema if the client is not vertexai.
  - Inlines the $defs.

  Example of a schema before and after (with mldev):
    Before:

    `schema`

    {
        'items': {
            '$ref': '#/$defs/CountryInfo'
        },
        'title': 'Placeholder',
        'type': 'array'
    }


    `defs`

    {
      'CountryInfo': {
        'properties': {
          'continent': {
              'title': 'Continent',
              'type': 'string'
          },
          'gdp': {
              'title': 'Gdp',
              'type': 'integer'}
          },
        }
        'required':['continent', 'gdp'],
        'title': 'CountryInfo',
        'type': 'object'
      }
    }

    After:

    `schema`
     {
        'items': {
          'properties': {
            'continent': {
                'type': 'string'
            },
            'gdp': {
                'type': 'integer'}
            },
          }
          'required':['continent', 'gdp'],
          'type': 'object'
        },
        'type': 'array'
    }
  """
  if client and not client.vertexai:
    schema.pop('title', None)

    if schema.get('default') is not None:
      raise ValueError(
          'Default value is not supported in the response schema for the Gemini API.'
      )

  if schema.get('title') == 'PlaceholderLiteralEnum':
    schema.pop('title', None)

  # If a dict is provided directly to response_schema, it may use `any_of`
  # instead of `anyOf`. Otherwise model_json_schema() uses `anyOf`
  if schema.get('any_of', None) is not None:
    schema['anyOf'] = schema.pop('any_of')

  if defs is None:
    defs = schema.pop('$defs', {})
    for _, sub_schema in defs.items():
      process_schema(sub_schema, client, defs)

  handle_null_fields(schema)

  # After removing null fields, Optional fields with only one possible type
  # will have a $ref key that needs to be flattened
  # For example: {'default': None, 'description': 'Name of the person', 'nullable': True, '$ref': '#/$defs/TestPerson'}
  if schema.get('$ref', None):
    ref = defs[schema.get('$ref').split('defs/')[-1]]
    for schema_key in list(ref.keys()):
      schema[schema_key] = ref[schema_key]
    del schema['$ref']

  any_of = schema.get('anyOf', None)
  if any_of is not None:
    if not client.vertexai:
      raise ValueError(
          'AnyOf is not supported in the response schema for the Gemini API.'
      )
    for sub_schema in any_of:
      # $ref is present in any_of if the schema is a union of Pydantic classes
      ref_key = sub_schema.get('$ref', None)
      if ref_key is None:
        process_schema(sub_schema, client, defs)
      else:
        ref = defs[ref_key.split('defs/')[-1]]
        any_of.append(ref)
    schema['anyOf'] = [item for item in any_of if '$ref' not in item]
    return

  schema_type = schema.get('type', None)
  if isinstance(schema_type, Enum):
    schema_type = schema_type.value
  schema_type = schema_type.upper()

  # model_json_schema() returns a schema with a 'const' field when a Literal with one value is provided as a pydantic field
  # For example `genre: Literal['action']` becomes: {'const': 'action', 'title': 'Genre', 'type': 'string'}
  const = schema.get('const', None)
  if const is not None:
    if schema_type == 'STRING':
      schema['enum'] = [const]
      del schema['const']
    else:
      raise ValueError('Literal values must be strings.')

  if schema_type == 'OBJECT':
    properties = schema.get('properties', None)
    if properties is None:
      return
    for name, sub_schema in properties.items():
      ref_key = sub_schema.get('$ref', None)
      if ref_key is None:
        process_schema(sub_schema, client, defs)
      else:
        ref = defs[ref_key.split('defs/')[-1]]
        process_schema(ref, client, defs)
        properties[name] = ref
    if (
        len(properties.items()) > 1
        and order_properties
        and all(
            ordering_key not in schema
            for ordering_key in ['property_ordering', 'propertyOrdering']
        )
    ):
      property_names = list(properties.keys())
      schema['property_ordering'] = property_names
  elif schema_type == 'ARRAY':
    sub_schema = schema.get('items', None)
    if sub_schema is None:
      return
    ref_key = sub_schema.get('$ref', None)
    if ref_key is None:
      process_schema(sub_schema, client, defs)
    else:
      ref = defs[ref_key.split('defs/')[-1]]
      process_schema(ref, client, defs)
      schema['items'] = ref


def _process_enum(
    enum: EnumMeta, client: Optional[_api_client.ApiClient] = None
) -> types.Schema:
  for member in enum:
    if not isinstance(member.value, str):
      raise TypeError(
          f'Enum member {member.name} value must be a string, got'
          f' {type(member.value)}'
      )

  class Placeholder(pydantic.BaseModel):
    placeholder: enum

  enum_schema = Placeholder.model_json_schema()
  process_schema(enum_schema, client)
  enum_schema = enum_schema['properties']['placeholder']
  return types.Schema.model_validate(enum_schema)


def t_schema(
    client: _api_client.ApiClient, origin: Union[types.SchemaUnionDict, Any]
) -> Optional[types.Schema]:
  if not origin:
    return None
  if isinstance(origin, dict):
    process_schema(origin, client, order_properties=False)
    return types.Schema.model_validate(origin)
  if isinstance(origin, EnumMeta):
    return _process_enum(origin, client)
  if isinstance(origin, types.Schema):
    if dict(origin) == dict(types.Schema()):
      # response_schema value was coerced to an empty Schema instance because it did not adhere to the Schema field annotation
      raise ValueError(f'Unsupported schema type.')
    schema = origin.model_dump(exclude_unset=True)
    process_schema(schema, client, order_properties=False)
    return types.Schema.model_validate(schema)

  if (
      # in Python 3.9 Generic alias list[int] counts as a type,
      # and breaks issubclass because it's not a class.
      not isinstance(origin, GenericAlias)
      and isinstance(origin, type)
      and issubclass(origin, pydantic.BaseModel)
  ):
    schema = origin.model_json_schema()
    process_schema(schema, client)
    return types.Schema.model_validate(schema)
  elif (
      isinstance(origin, GenericAlias)
      or isinstance(origin, type)
      or isinstance(origin, VersionedUnionType)
      or typing.get_origin(origin) in _UNION_TYPES
  ):

    class Placeholder(pydantic.BaseModel):
      placeholder: origin

    schema = Placeholder.model_json_schema()
    process_schema(schema, client)
    schema = schema['properties']['placeholder']
    return types.Schema.model_validate(schema)

  raise ValueError(f'Unsupported schema type: {origin}')


def t_speech_config(
    _: _api_client.ApiClient, origin: Union[types.SpeechConfigUnionDict, Any]
) -> Optional[types.SpeechConfig]:
  if not origin:
    return None
  if isinstance(origin, types.SpeechConfig):
    return origin
  if isinstance(origin, str):
    return types.SpeechConfig(
        voice_config=types.VoiceConfig(
            prebuilt_voice_config=types.PrebuiltVoiceConfig(voice_name=origin)
        )
    )
  if (
      isinstance(origin, dict)
      and 'voice_config' in origin
      and 'prebuilt_voice_config' in origin['voice_config']
  ):
    return types.SpeechConfig(
        voice_config=types.VoiceConfig(
            prebuilt_voice_config=types.PrebuiltVoiceConfig(
                voice_name=origin['voice_config']['prebuilt_voice_config'].get(
                    'voice_name'
                )
            )
        )
    )
  raise ValueError(f'Unsupported speechConfig type: {type(origin)}')


def t_tool(client: _api_client.ApiClient, origin) -> types.Tool:
  if not origin:
    return None
  if inspect.isfunction(origin) or inspect.ismethod(origin):
    return types.Tool(
        function_declarations=[
            types.FunctionDeclaration.from_callable(
                client=client, callable=origin
            )
        ]
    )
  else:
    return origin


# Only support functions now.
def t_tools(
    client: _api_client.ApiClient, origin: list[Any]
) -> list[types.Tool]:
  if not origin:
    return []
  function_tool = types.Tool(function_declarations=[])
  tools = []
  for tool in origin:
    transformed_tool = t_tool(client, tool)
    # All functions should be merged into one tool.
    if transformed_tool.function_declarations:
      function_tool.function_declarations += (
          transformed_tool.function_declarations
      )
    else:
      tools.append(transformed_tool)
  if function_tool.function_declarations:
    tools.append(function_tool)
  return tools


def t_cached_content_name(client: _api_client.ApiClient, name: str):
  return _resource_name(client, name, collection_identifier='cachedContents')


def t_batch_job_source(client: _api_client.ApiClient, src: str):
  if src.startswith('gs://'):
    return types.BatchJobSource(
        format='jsonl',
        gcs_uri=[src],
    )
  elif src.startswith('bq://'):
    return types.BatchJobSource(
        format='bigquery',
        bigquery_uri=src,
    )
  else:
    raise ValueError(f'Unsupported source: {src}')


def t_batch_job_destination(client: _api_client.ApiClient, dest: str):
  if dest.startswith('gs://'):
    return types.BatchJobDestination(
        format='jsonl',
        gcs_uri=dest,
    )
  elif dest.startswith('bq://'):
    return types.BatchJobDestination(
        format='bigquery',
        bigquery_uri=dest,
    )
  else:
    raise ValueError(f'Unsupported destination: {dest}')


def t_batch_job_name(client: _api_client.ApiClient, name: str):
  if not client.vertexai:
    return name

  pattern = r'^projects/[^/]+/locations/[^/]+/batchPredictionJobs/[^/]+$'
  if re.match(pattern, name):
    return name.split('/')[-1]
  elif name.isdigit():
    return name
  else:
    raise ValueError(f'Invalid batch job name: {name}.')


LRO_POLLING_INITIAL_DELAY_SECONDS = 1.0
LRO_POLLING_MAXIMUM_DELAY_SECONDS = 20.0
LRO_POLLING_TIMEOUT_SECONDS = 900.0
LRO_POLLING_MULTIPLIER = 1.5


def t_resolve_operation(api_client: _api_client.ApiClient, struct: dict):
  if (name := struct.get('name')) and '/operations/' in name:
    operation: dict[str, Any] = struct
    total_seconds = 0.0
    delay_seconds = LRO_POLLING_INITIAL_DELAY_SECONDS
    while operation.get('done') != True:
      if total_seconds > LRO_POLLING_TIMEOUT_SECONDS:
        raise RuntimeError(f'Operation {name} timed out.\n{operation}')
      # TODO(b/374433890): Replace with LRO module once it's available.
      operation: dict[str, Any] = api_client.request(
          http_method='GET', path=name, request_dict={}
      )
      time.sleep(delay_seconds)
      total_seconds += total_seconds
      # Exponential backoff
      delay_seconds = min(
          delay_seconds * LRO_POLLING_MULTIPLIER,
          LRO_POLLING_MAXIMUM_DELAY_SECONDS,
      )
    if error := operation.get('error'):
      raise RuntimeError(
          f'Operation {name} failed with error: {error}.\n{operation}'
      )
    return operation.get('response')
  else:
    return struct


def t_file_name(
    api_client: _api_client.ApiClient, name: Union[str, types.File]
):
  # Remove the files/ prefix since it's added to the url path.
  if isinstance(name, types.File):
    name = name.name

  if name is None:
    raise ValueError('File name is required.')

  if name.startswith('https://'):
    suffix = name.split('files/')[1]
    match = re.match('[a-z0-9]+', suffix)
    if match is None:
      raise ValueError(f'Could not extract file name from URI: {name}')
    name = match.group(0)
  elif name.startswith('files/'):
    name = name.split('files/')[1]

  return name


def t_tuning_job_status(
    api_client: _api_client.ApiClient, status: str
) -> types.JobState:
  if status == 'STATE_UNSPECIFIED':
    return 'JOB_STATE_UNSPECIFIED'
  elif status == 'CREATING':
    return 'JOB_STATE_RUNNING'
  elif status == 'ACTIVE':
    return 'JOB_STATE_SUCCEEDED'
  elif status == 'FAILED':
    return 'JOB_STATE_FAILED'
  else:
    return status


# Some fields don't accept url safe base64 encoding.
# We shouldn't use this transformer if the backend adhere to Cloud Type
# format https://cloud.google.com/docs/discovery/type-format.
# TODO(b/389133914,b/390320301): Remove the hack after backend fix the issue.
def t_bytes(api_client: _api_client.ApiClient, data: bytes) -> str:
  if not isinstance(data, bytes):
    return data
  return base64.b64encode(data).decode('ascii')
